![]() Ridge Counts for Whorls: Even if the little finger has a whorl, the ridge counting value is evaluated not ridge. And given the uniqueness and accessibility of fingerprints, the process of using fingerprint identification technology is easy and effective for enhancing the security protocols you have in place. As you can see, there are several types of fingerprints. No loops in either of the little fingers: Whorls are used and preference should be given to the right little finger. Tented Arch: The arch lies in the center ridges and does not show a continuous arch. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively. Rule 2: Right Little finger has no loop: Ridge count values are taken from the left little fingers. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. ![]() It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. ![]() This eliminates identity recognition manual work and enables automated processing. Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness.
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